Applying polypharmacology approach for drug repurposing for SARS-CoV2.

Drug repurposing for SARS-CoV-2 main protease: Molecular docking and molecular dynamics investigations

The identification of new viral drugs has become a task of paramount significance due to the frequent occurrence of viral infections and especially during the current pandemic. Despite the recent advancements, the development of antiviral drugs has not made parallel progress. Reduction of time frame and cost of the drug development process is the major advantage of drug repurposing. Therefore, in this study, a drug repurposing strategy using molecular modelling techniques, i.e. biological activity prediction, virtual screening, and molecular dynamics simulation was employed to find promising repurposing candidates for viral infectious diseases. The biological activities of non-redundant (4171) drug molecules were predicted using PASS analysis, and 1401 drug molecules were selected which showed antiviral activities in the analysis. These drug molecules were subjected to virtual screening against the selected non-structural viral proteins. A series of filters, i.e. top 10 drug molecules based on binding affinity, mean value of binding affinity, visual inspection of protein-drug complexes, and number of H-bond between protein and drug molecules were used to narrow down the drug molecules. Molecular dynamics simulation analysis was carried out to validate the intrinsic atomic interactions and binding conformations of protein-drug complexes. The binding free energies of drug molecules were assessed by employing MMPBSA analysis. Finally, nine drug molecules were prioritized, as promising repurposing candidates with the potential to inhibit the selected non-structural viral proteins. Communicated by Ramaswamy H. Sarma

Human metapneumovirus (hMPV) primarily causes respiratory tract infections in young children and older adults. According to the 2024 Human Pneumonia Etiology Research for Child Health (PERCH) study, hMPV is the second leading common cause of pneumonia in children under five in Asia and Africa. The virus encodes nine proteins, including the essential Fusion (F) and G glycoproteins, which facilitate entry to the host cells. Currently, there are no approved vaccines or antiviral treatments for hMPV; supportive care is the primary way it is managed. Hence, this study focuses on the F protein as a therapeutic target to find a repurposable drug to fight hMPV. Refolding of the F protein and its binding to heparan sulfate enable hMPV infection. Heparin sulfate is important for hMPV binding, and we have found that cangrelor and AVN 944 can prevent the fusion of membranes. We developed a deep learning-based pharmacophore to identify potential drugs targeting hMPV, from which we could narrowed a list of 2400 FDA-approved drugs and 255 antiviral drugs to 792 and 72 drugs, respectively. We then conducted quantitative validation using the ROC curve. Further virtual screening of the drugs was performed, leading us to select the one with the highest docking score. The validation of the deep learning prediction in virtual screening Pearson correlation was done. Further, the MD simulation of these drugs confirmed that the protein-drug complex stability remained in dynamic condition. Further, the stability of protein-drug complexes than unbound protein was confirmed by Free Energy Landscape and Dynamic Cross Correlation Matrices. Further in vitro and in vivo experiments need to determine the efficacy of the identified candidates.

Coronavirus disease 2019 (COVID-19) caused appalling conditions over the globe, which is currently faced by the entire human population. One of the primary reasons behind the uncontrollable situation is the lack of specific therapeutics. In such conditions, drug repurposing of available drugs (viz. Chloroquine, Lopinavir, etc.) has been proposed, but various clinical and preclinical investigations indicated the toxicity and adverse side effects of these drugs. This study explores the inhibition potency of phytochemicals from Tinospora cordifolia (Giloy) against SARS CoV-2 drugable targets (spike glycoprotein and Mpro proteins) using molecular docking and MD simulation studies. ADMET, virtual screening, MD simulation, postsimulation analysis (RMSD, RMSF, Rg, SASA, PCA, FES) and MM-PBSA calculations were carried out to predict the inhibition efficacy of the phytochemicals against SARS CoV-2 targets. Tinospora compounds showed better binding affinity than the corresponding reference. Their binding affinity ranges from –9.63 to –5.68 kcal/mole with spike protein and –10.27 to –7.25 kcal/mole with main protease. Further 100 ns exhaustive simulation studies and MM-PBSA calculations supported favorable and stable binding of them. This work identifies Nine Tinospora compounds as potential inhibitors. Among those, 7-desacetoxy-6,7-dehydrogedunin was found to inhibit both spike (7NEG) and Mpro (7MGS and 6LU7) proteins, and Columbin was found to inhibit selected spike targets (7NEG and 7NX7). In all the analyses, these compounds performed well and confirms the stable binding. Hence the identified compounds, advocated as potential inhibitors can be taken for further in vitro and in vivo experimental validation to determine their anti-SARS-CoV-2 potential. Communicated by Ramaswamy H. Sarma

Background/objectivesColorectal cancer is characterized by various oncogenic mutations, with the KRAS G12D mutation being the most prevalent. The development of MRTX1133 has revitalized the KRAS direct targeting. However, colorectal cancer demonstrated intrinsic resistance to MRTX1133, primarily due to the feedback activation of the EGFR pathway. Combining KRAS G12D and EGFR inhibition has demonstrated improved treatment efficacy, highlighting the potential of dual-targeting approaches in colorectal cancer therapy. This study employs CADD tools to identify approved drugs capable of dual targeting KRAS G12D and EGFR.MethodsA library of 3,591 approved drugs was screened against KRAS G12D using high-throughput virtual screening (HTVs), standard precision (SP) and extra precision (XP) Glide docking modules. The top-ranking compounds were then docked into the EGFR binding pocket using XP mode, and docking scores were calculated. Further refinement of binding affinities was performed using Molecular Mechanics with Generalized Born and Surface Area Solvation (MM-GBSA) and Molecular Dynamics (MD) simulations.ResultsA total of 25 drugs showed better affinity to KRAS G12D than MRTX1133 (−9.18 kcal/mol). Among them, six drugs: Reproterol, Macimorelin, Nebivolol, Nadolol, Antrafenine, and Carteolol, displayed docking scores between −7.186 and −8.864 kcal/mol against EGFR, compared to the reference ligand Erlotinib (−9.669 kcal/mol). Subsequent MM-GBSA and MD identified Carteolol, an FDA-approved beta blocker, as the most promising candidate, showing stable and reliable binding with KRAS G12D and relatively stable interactions with EGFR.ConclusionsThis in silico study predicts Carteolol as a potential dual-targeting therapeutic agent, requiring biochemical and cellular validation before clinical relevance can be established.

The pharmaceutical industry has undergone a severe economic crunch in antibiotic discovery research due to evolving bacterial resistance along with enormous time and money that gets consumed in de novo drug design and discovery strategies. Nevertheless, drug repurposing has evolved as an economically safer and excellent alternative strategy to identify approved drugs for new therapeutic indications. Virtual high throughput screening (vHTS) and phenotype-based high throughput screening (HTS) of approved molecules play a crucial role in identifying, developing, and repurposing old drug molecules into anti-infective agents either alone or in synergistic combination with antibiotic therapy. This chapter briefly explains the process of drug repurposing/repositioning in comparison to de novo methods utilizing vHTS and HTS technologies along with ‘omics- and poly-pharmacology-based drug repurposing strategies in the identification and development of anti-microbial agents. This chapter also gives an insightful survey of the intellectual property landscape on drug repurposing. Further, the challenges and applications of drug repurposing strategies in the discovery of anti-infective drugs are exemplified. The future perspectives of drug repurposing in the context of anti-infective agents are also discussed.

The use of FDA-approved drugs for the therapy of lung cancer through drug repurposing is a noteworthy approach. We retrieved all the FDA-approved triazole-based drugs from Drugbank and conducted docking-based virtual screening of the triazole-based FDA-approved drugs against the EGFR target. Deferasirox demonstrated hydrogen bonding interactions with residues Thr 830, Asp 831, Lys 721 and Met 769 of the EGFR-TKD receptor (PDB ID: 1M17) and Posaconazole showed hydrogen bonding with residues Met 769 and Glu 734 of the similar EGFR receptor along with the binding energies of −9.60, −9.50, kcal/mol respectively. The dock score for reference molecule found to be −6.70 (kcal/mol). Best two ligands (Deferasirox and Posaconazole) were selected on the basis of dock score from the virtual screening results for in vitro NRU assay using A549 cells to determine their cytotoxicity and cell viability. During the in vitro NRU experiment, Deferasirox and Posaconazole demonstrated IC50 values of 114.9 and 910.2 µM, respectively. MD simulations were performed to investigate the dynamic behaviour and stability, and interactions were compared to the standard inhibitor for the EGFR target. DFT studies were carried out to determine their molecular properties, including their electronic structure, bond lengths and bond energies. The results of the in silico and in vitro studies were analysed to assess the potential of Deferasirox and Posaconazole for use as anticancer agents in the mitigation of lung cancer symptoms. This study focused on repurposing FDA-approved triazole-based compounds to identify their potential as effective lung cancer treatments with anticancer properties.

Simple SummaryWith the urgent necessity of potential treatment against novel coronavirus disease, we used several computational methods to search for active drugs from an extensive database. The results of our investigation suggested several established drugs that can be subjected to further analysis for the treatment of novel coronavirus disease. Various methods used in this study proved the effectiveness of the retrieved drugs. Therefore, our findings highly recommend the mentioned drugs to be scrutinized to discover drugs against novel coronavirus.Novel coronavirus disease (COVID-19) was identified from China in December 2019 and spread rapidly through human-to-human transmission, affecting so many people worldwide. Until now, there has been no specific treatment against the disease and repurposing of the drug. Our investigation aimed to screen potential inhibitors against coronavirus for the repurposing of drugs. Our study analyzed sequence comparison among SARS-CoV, SARS-CoV-2, and MERS-CoV to determine the identity matrix using discovery studio. SARS-CoV-2 Mpro was targeted to generate an E-pharmacophore hypothesis to screen drugs from the DrugBank database having similar features. Promising drugs were used for docking-based virtual screening at several precisions. Best hits from virtual screening were subjected to MM/GBSA analysis to evaluate binding free energy, followed by the analysis of binding interactions. Furthermore, the molecular dynamics simulation approaches were carried out to assess the docked complex’s conformational stability. A total of 33 drug classes were found from virtual screening based on their docking scores. Among them, seven potential drugs with several anticancer, antibiotic, and immunometabolic categories were screened and showed promising MM/GBSA scores. During interaction analysis, these drugs exhibited different types of hydrogen and hydrophobic interactions with amino acid residue. Besides, 17 experimental drugs selected from virtual screening might be crucial for drug discovery against COVID-19. The RMSD, RMSF, SASA, Rg, and MM/PBSA descriptors from molecular dynamics simulation confirmed the complex’s firm nature. Seven promising drugs for repurposing against SARS-CoV-2 main protease (Mpro), namely sapanisertib, ornidazole, napabucasin, lenalidomide, daniquidone, indoximod, and salicylamide, could be vital for the treatment of COVID-19. However, extensive in vivo and in vitro studies are required to evaluate the mentioned drug’s activity.

Monkeypox viral infection is an emerging threat and a major concern for the human population. The lack of drug molecules to treat this disease may worsen the problem. Identifying potential drug targets can significantly improve the process of developing potent drug molecules for treating monkeypox. The proteins responsible for viral replication are attractive drug targets. Identifying potential inhibitors from known drug molecules that target these proteins can be key to finding a cure for monkeypox. In this work, two viral proteins, DNA-dependent RNA polymerase (DdRp) and viral core cysteine proteinase, were considered as potential drug targets. Sixteen antibiotic drugs from the tetracycline class were screened against both viral proteins through high-throughput virtual screening. These tetracycline class of antibiotic drugs have the ability to inhibit bacterial protein synthesis, which makes these antibiotics drugs a prominent candidate for drug repurposing. Based on the screening result obtained against DdRp, top two compounds, namely Tigecycline and Eravacycline with docking scores of − 8.88 and − 7.87 kcal/mol, respectively, were selected for further analysis. Omadacycline and minocycline, with docking scores of − 10.60 and − 7.51 kcal/mol, are the top two compounds obtained after screening proteinase with the drug library. These compounds, along with reference compounds GTP for DdRp and tecovirimat for proteinase, were used to form protein–ligand complexes, followed by their evaluation through a 300 ns molecular dynamic simulation. The MM/GBSA binding free energy calculation and principal components analysis of these selected complexes were also conducted for understanding the dynamic stability and binding affinity of these compounds with respective target proteins. Overall, this study demonstrates the repurposing of tetracycline-derived drugs as a therapeutic solution for monkeypox viral infection.

Drug repurposing is the modern technology for identifying the new therapeutic property of approved and operational drug. De novo drug discovery faces many challenges including clinical trials, time and cost. To overcome these aspects drug repurposing has evolved as promising approach, that has benefits like previously studied safety profiles and bypassing the early developmental changes and accelerating clinical trials by reducing the cost up to 60%. "Omics" technologies, computational capabilities imbibed with the artificial intelligence caters the drug repurposing for new targets or pathogens. By using virtual screening, molecular docking, and sequence similarity maps, researchers can identify multiple therapeutic effects by a drug. In the year 2025, 47 drugs were approved by FDA after drug repurposing. Nearly 45% of drugs repurposing activity in 2025 were focused on treatment of cancers. The global scale drug repurposing market has been increasing continuously and reached $36.87 billion in 2025. In this review we focus on how drug repurposing activity enhances the use of approved drug for treatment of variety of diseases or targets.

Globally, traditional and complementary therapies (such as homeopathy, phytotherapy and herbal medications) are becoming increasingly prevalent alongside modern medical care. The therapeutic substances used in homeopathy and other traditional complementary medicine disciplines are derived from these traditional applications. Numerous notable clinical and preclinical studies have shown their impact during COVID-19. This study aims to investigate the potential antiviral effects of medicinal alkaloids against the monkeypox virus in the current scenario. The structures of 47 known phytochemicals of commonly used medicines in CAM were obtained from PubChem in SDF format, minimized, and then docked against the 3D crystal structure of monkeypox virus methyltransferase VP39 (PDB: 8B07). The results were analyzed, and compounds with significant docking scores were further evaluated. The docking results showed that six compounds-Sarsaponin, Luteolin, Quercetin, Apigenin, and Ducimarine-had better docking scores than Tecovirimat, a standard drug used in managing monkeypox. Additionally, most compounds exhibited better docking scores than Cidofovir, another drug used against monkeypox. Interaction analysis revealed that hydrogen bonding, pi-pi T-shaped, and pi-alkyl interactions were responsible for the observed docking scores. Key amino acid residues involved in the interaction between the compounds and monkeypox virus methyltransferase VP39 included GLY 96, LEU 159, PHE 115, VAL 139, VAL 116, GLY 68, ARG 140, ILE 94, ASN 156, and ARG 156. Five phytochemicals-Luteolin, Quercetin, Apigenin, Sarsaponin, and Ducimarine-show strong potential as monkeypox virus methyltransferase inhibitors. Apigenin and Ducimarine are particularly promising due to their favorable profiles, including no PAINS alerts and good drug-like properties. Sarsaponin, while highly permeable, has high lipophilicity, which may limit its use. Luteolin and Quercetin also show potential but require further investigation due to PAINS alerts.

Identification of potential inhibitors against nuclear Dam1 complex subunit Ask1 of Candida albicans using virtual screening and MD simulations

Docking and scoring are critical issues in virtual drug screening methods. Fast and reliable methods are required for the prediction of binding affinity especially when applied to a large library of compounds. The implementation of receptor flexibility and refinement of scoring functions for this purpose are extremely challenging in terms of computational speed. Here we propose a knowledge-based multiple-conformation docking method that efficiently accommodates receptor flexibility thus permitting reliable virtual screening of large compound libraries. Starting with a small number of active compounds, a preliminary docking operation is conducted on a large ensemble of receptor conformations to select the minimal subset of receptor conformations that provides a strong correlation between the experimental binding affinity (e.g., Ki, IC50) and the docking score. Only this subset is used for subsequent multiple-conformation docking of the entire data set of library (test) compounds. In conjunction with the multiple-conformation docking procedure, a two-step scoring scheme is employed by which the optimal scoring geometries obtained from the multiple-conformation docking are re-scored by a molecular mechanics energy function including desolvation terms. To demonstrate the feasibility of this approach, we applied this integrated approach to the estrogen receptor alpha (ERalpha) system for which published binding affinity data were available for a series of structurally diverse chemicals. The statistical correlation between docking scores and experimental values was significantly improved from those of single-conformation dockings. This approach led to substantial enrichment of the virtual screening conducted on mixtures of active and inactive ERalpha compounds.

Identification and investigation of hits targeting the N-methyl-D-aspartate receptor via drug repurposing: A plausible approach for anti-Alzheimer drug discovery.

High-throughput virtual screening of Streptomyces spp. metabolites as antiviral inhibitors against the Nipah virus matrix protein